DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models

ArXiv ID: 2403.14063 “View on arXiv”

Authors: Unknown

Abstract

In this work, we propose an approach to generalize denoising diffusion probabilistic models for stock market predictions and portfolio management. Present works have demonstrated the efficacy of modeling interstock relations for market time-series forecasting and utilized Graph-based learning models for value prediction and portfolio management. Though convincing, these deterministic approaches still fall short of handling uncertainties i.e., due to the low signal-to-noise ratio of the financial data, it is quite challenging to learn effective deterministic models. Since the probabilistic methods have shown to effectively emulate higher uncertainties for time-series predictions. To this end, we showcase effective utilisation of Denoising Diffusion Probabilistic Models (DDPM), to develop an architecture for providing better market predictions conditioned on the historical financial indicators and inter-stock relations. Additionally, we also provide a novel deterministic architecture MaTCHS which uses Masked Relational Transformer(MRT) to exploit inter-stock relations along with historical stock features. We demonstrate that our model achieves SOTA performance for movement predication and Portfolio management.

Keywords: denoising diffusion probabilistic models, portfolio management, time-series forecasting, graph learning, financial prediction

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper heavily utilizes advanced mathematical frameworks including diffusion models, graph theory, and transformer architectures with detailed formula derivations. However, it lacks reported implementation specifics like code, backtests, or real-world data metrics, focusing instead on theoretical model architecture and proposed methodology.

  flowchart TD
    A["Research Goal: Probabilistic Stock Prediction<br>Using Diffusion Models"] --> B["Data: Historical Stock Prices &<br>Inter-stock Relations via Graphs"]
    B --> C{"Model Architecture"}
    C --> D["DiffSTOCK: DDPM for<br>Probabilistic Forecasting"]
    C --> E["MaTCHS: Masked Relational<br>Transformer for Deterministic Prediction"]
    D --> F["Training: Learning Noise Schedules<br>& Denoising Processes"]
    E --> F
    F --> G["Key Findings: SOTA Performance<br>in Movement Prediction &<br>Portfolio Management"]